FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding-Reference-Cited by-同舟云学术

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FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding

Published:2024-05-20 Issue: Volume:31 Page:821-830
ISSN:
Container-title:IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
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Author:

Guan Yixuan¹,Liu Xuefeng¹,Niu Jianwei¹,Ren Tao²

Affiliation:

1. Beihang University,State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beijing,China

2. Chinese Academy of Sciences,State Key Laboratory of Intelligent Game, Institute of Software,Beijing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10621050/10621073/10621176.pdf?arnumber=10621176

Reference32 articles.

1. Deep gradient compression: Reducing the communication bandwidth for distributed training;Lin;ICLR,2018

2. Sparsified sgd with memory;Stich,2018

3. Qsgd: Communication-efficient sgd via gradient quantization and encoding;Alistarh;NeurIPS,2017

4. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization;Reisizadeh;AISTATS,2020

5. Rethinking gradient sparsification as total error minimization;Sahu;NeurIPS,2021

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